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Advancing Agile Software Cost Estimation Through Data Synthesis: A Comparative Analysis of Five Generation Techniques

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IEEE ACCESS 2025年 13卷 63219-63236页

作者： Zhao, Xiaoyan Mansor, Zulkefli Razali, Rozilawati Nazri, Mohd Zakree Ahmad Xiong, Xin Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Ctr Software Technol & Management Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia

Agile has been used in software development for over 20 years and is the preferred development method for more than 85% of software companies. However, cost estimation in agile development remains a significant challenge. This is reflected in the fact that the accuracy of estimation still needs improvement, and most cost estimation techniques still rely on the team's experience and knowledge. While machine learning algorithms have performed better in this area, the lack of sufficient agile cost data hinders large-scale training and in-depth research. To address this issue, this study selected five data generation techniques-variational autoencoder (VAE), Wasserstein Generative Adversarial Network (WGAN), Synthetic Minority Over-sampling Technique for Nominal and Continuous Features (SMOTE-NC), Data Augmentation for Tabular Data (Augmentation), and Tabular Data Diffusion Probabilistic Models (TabDDPM)-based on the characteristics of agile cost data. Using cost data from 75 agile projects, these techniques were employed to generate three sets of data with sizes of 200, 500, and 1000. A performance evaluation model was created based on consistency, authenticity, diversity, and effectiveness to verify the performance of these generated data. The experimental results show that WGAN consistently scored 16 out of 20 points across all three data sets, excelling in data consistency and authenticity. SMOTE-NC and Augmentation Were followed. SMOTE-NC scored 15 out of 20 points for all data sizes and performed best in terms of effectiveness, with an MMRE of 88.16% and a PRED (0.2) of 84.5%. Augmentation performed the best when generating 1000 data points. These findings highlight the potential of data generation technologies, particularly WGAN, in enhancing agile cost estimation and providing guidance on selecting the appropriate amount of data. This lays a foundation for further development of machine learning algorithms in this field and offers valuable insights for other res

关键词： Costs Estimation Generative adversarial networks Training Agile software development Data models Hidden Markov models Data collection Data augmentation autoencoders Data generation technique software cost estimation agile software development variational autoencoder Wasserstein generative adversarial network synthetic minority over-sampling technique for nominal and continuous features data augmentation for tabular data tabular data diffusion probabilistic model

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LPUWF-LDM: Enhanced latent diffusion model for precise late-phase UWF-FA generation on limited dataset

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 270卷

作者： Fang, Zhaojie Yu, Xiao Zhou, Guanyu Zhuang, Ke Chen, Yifei Ge, Ruiquan Wang, Changmiao Jia, Gangyong Wu, Qing Ye, Juan Nuliqiman, Maimaiti Xu, Peifang Elazab, Ahmed Hangzhou Dianzi Univ Hangzhou 310018 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen 518037 Peoples R China Misr Higher Inst Commerce & Comp Mansoura 35516 Egypt Zhejiang Univ Affiliated Hosp 2 Eye Ctr Zhejiang Prov Key Lab OphthalmolSch Med Hangzhou 310000 Peoples R China

Ultra-Wide-Field Fluorescein Angiography (UWF-FA) enables precise identification of ocular diseases using sodium fluorescein, which can be potentially harmful. Existing research has developed methods to generate UWF-FA from Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) to reduce the adverse reactions associated with injections. However, these methods have been less effective in producing high-quality late- phase UWF-FA, particularly in lesion areas and fine details. Two primary challenges hinder the generation of high-quality late-phase UWF-FA: the scarcity of paired UWF-SLO and early/late-phase UWF-FA datasets, and the need for realistic generation at lesion sites and potential blood leakage regions. This study introduces an improved latent diffusion model framework to generate high-quality late-phase UWF-FA from limited paired UWF images. To address the challenges as mentioned earlier, our approach employs a module utilizing Cross- temporal Regional Difference Loss, which encourages the model to focus on the differences between early and late phases. Additionally, we introduce a low-frequency enhanced noise strategy in the diffusion forward process to improve the realism of medical images. To further enhance the mapping capability of the variational autoencoder module, especially with limited datasets, we implement a gated convolutional encoder to extract additional information from conditional images. Our Latent Diffusion Model for Ultra-Wide-Field Late-Phase Fluorescein Angiography (LPUWF-LDM) effectively reconstructs fine details in late-phase UWF-FA and achieves state-of-the-art results compared to other existing methods when working with limited datasets. Our source code is available at: https://***/Tinysqua/LPUWF-LDM.

关键词： Diffusion model Loss enhancement Cross-modal generation Ultra-wide-field fundus photo variational autoencoder

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Characterizing Marine Magnetic Anomalies: A Machine Learning Approach to Advancing the Understanding of Oceanic Crust Formation

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JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2025年第2期130卷

作者： Wu, S. Thoram, S. Sun, J. Sager, W. W. Chen, J. Univ Houston Dept Earth & Atmospher Sci Houston TX 77004 USA Univ Houston Dept Elect & Comp Engn Houston TX USA

Linear magnetic anomalies (LMA), resulting from Earth's magnetic field reversals recorded by seafloor spreading serve as crucial evidence for oceanic crust formation and plate tectonics. Traditionally, LMA analysis relies on visual inspection and manual interpretation, which can be subject to biases due to the complexities of the tectonic history, uneven data coverage, and strong local anomalies associated with seamounts and fracture zones. In this study, we present a Machine learning (ML)-based framework to identify LMA, determine their orientations and distinguish spatial patterns across oceans. The framework consists of three stages and is semi-automated, scalable and unbiased. First, a generation network produces artificial yet realistic magnetic anomalies based on user-specified conditions of linearity and orientation, addressing the scarcity of the labeled training dataset for supervised ML approaches. Second, a characterization network is trained on these generated magnetic anomalies to identify LMA and their orientations. Third, the detected LMA features are clustered into groups based on predicted orientations, revealing underlying spatial patterns, which are directly related to propagating ridges and tectonic activity. The application of this framework to magnetic data from seven areas in the Atlantic and Pacific oceans aligns well with established magnetic lineations and geological features, such as the Mid-Atlantic Ridge, Reykjanes Ridge, Galapagos Spreading Center, Shatsky Rise, Juan de Fuca Ridge and even Easter Microplate and Galapagos hotspot. The proposed framework establishes a solid foundation for future data-driven marine magnetic analyses and facilitates objective and quantitative geological interpretation, thus offering the potential to enhance our understanding of oceanic crust formation.

关键词： marine magnetic anomalies machine learning linear magnetic anomaly oceanic crust formation variational autoencoder

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Knowledge-embedding deep interpretable graph model for wear prediction: Application in pantograph-catenary systems

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 261卷

作者： Mo, Yutao Peng, Yizhen Wu, Jing Fan, Kangbo Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Rail Transit Grp Co LTD Chongqing 400042 Peoples R China

The wear modeling and life prediction of pantographs are crucial for ensuring the safety and reliability of urban rail transit systems. However, because of the complex interplay between stochastic vibrations and electrical currents, pantograph wear exhibits strong variability, and physics-based degradation predictions based solely on material parameters, environmental factors, and wear mechanisms are limited in accuracy. Purely data-driven approaches, on the other hand, are constrained by their reliance on large datasets and lack of interpretability, making them difficult to meet practical engineering needs. To address these challenges, we propose an interpretable variational model called IV-NBEATS. This study integrates the surface wear mechanism under the asperity hypothesis into the N-BEATS model using the projection principle, thereby enhancing the interpretability of the model. In addition, we introduce a method for describing the uncertainty of key wear parameters, enabling a deep network to represent the uncertainty of these parameters. Furthermore, to cope with dynamic changes in wear system parameters, we propose a dynamic updating method based on a Bayesian directed graph model that effectively overcomes the limitations of existing methods in capturing the temporal evolution of wear system parameters. Finally, the effectiveness of the proposed approach is demonstrated through the analysis of a real-world case study of pantograph wear.

关键词： Pantograph wear N -BEATS variational autoencoder Interpretability

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DiffuseVAE plus plus : Mitigating training-sampling mismatch based on additional noise for higher fidelity image generation

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NEUROCOMPUTING 2025年 633卷

作者： Yang, Xiaobao Luo, Wei Ning, Hailong Zhang, Guorui Sun, Wei Ma, Sugang Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China

Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated remarkable results in image generation. However, there exist a mismatch between the training and sampling process in current diffusion models, in addition, the U-Net denoising network based on simple residual blocks cannot predict noise information accurately, which affects the generated quality. To address these limitations, we present a novel image generation method that achieves higher fidelity. First, by additionally adding the standard Gaussian noise in the diffusion forward process, which does not disrupt the forward process, our method alleviates the mismatch. Subsequently, an important efficient denoising network based on U-Net is presented, where our proposed Simple Squeeze-Excitation and Simple GLU, combined with Depthwise Separable Convolution, enhance the ability of the model to predict real noise using the Simplified Nonlinear No Activation (SNNA) block. Furthermore, considering the structural characteristics of the baseline model, we introduce an additional cross-attention mechanism to enable DDPM to focus on VAE stage characteristics. Allowing the model to more accurately capture and learn the noise information. Finally, it is shown after extensive experiments the proposed DiffuseVAE++ obtains significant gains in FID scores, improving from 3.84 to 2.41 on CIFAR-10 and from 3.94 to 2.30 on CelebA-64. In particular, the IS scores on CIFAR-10 reaches 10.10, which is comparable to the current state-of-the-art methods competitively (e.g., U-ViT, StyleGAN2).

关键词： Image generation Generative model Diffusion model variational autoencoder Unconditional generation

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Generative-based hybrid model with semantic representations for generalized zero-shot learning

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第1期19卷 1-12页

作者： Akdemir, Emre Barisci, Necaattin Gazi Univ Grad Sch Informat Dept Comp Sci Ankara Turkiye Minist Natl Educ Directorate Gen Informat Technol Ankara Turkiye Gazi Univ Fac Technol Dept Comp Engn Ankara Turkiye

Generalized Zero-Shot Learning (GZSL) endeavors to recognize instances of seen and unseen classes using semantic information and labeled instances of only seen classes. In addressing the data imbalance problem in GZSL, generator networks produce synthetic features out of visual features and semantic features. However, the original semantic features exhibit insufficient distinctiveness. Throughout the current study, a hybrid generative-based GZSL framework that combines semantic descriptions used in fine-grained data sets with semantic attributes is proposed to increase the distinctiveness of the original semantic features. The proposed model offers semantic information enriched by the use of semantic attributes and semantic descriptions. A generalized classification is provided by using Generative Adversarial Network (GAN) and variational autoencoder (VAE) to generate visual features of unseen classes from this semantic information. The proposed method has been evaluated through extensive experiments on AWA2, CUB, FLO and SUN GZSL benchmark datasets. The experiments showed AWA2 70.96, CUB 76.71, FLO 92.12 and SUN 42.84% performance. Notably the proposed method achieved superior GZSL results, 3.41% and 3.52% on fine-grained CUB and FLO datasets respectively.

关键词： Generalized zero-shot learning Semantic representation variational autoencoder Generative adversarial network

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Generating practical adversarial examples against learning-based network intrusion detection systems

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ANNALS OF TELECOMMUNICATIONS 2025年第3-4期80卷 209-226页

作者： Kumar, Vivek Kumar, Kamal Singh, Maheep Natl Inst Technol Dept Comp Sci & Engn Srinagar 246174 Uttarakhand India THDC Inst Hydropower Engn & Technol Dept Comp Sci & Engn Tehri 249124 Uttarakhand India IGDTUW Dept Informat Technol Delhi 110006 India

There has been a significant development in the design of intrusion detection systems (IDS) by using deep learning (DL)/machine learning (ML) methods for detecting threats in a computer network. Unfortunately, these DL/ML-based IDS are vulnerable to adversarial examples, wherein a malicious data sample can be slightly perturbed to cause a misclassification by an IDS while retaining its malicious properties. Unlike image recognition domain, the network domain has certain constraints known as domain constraints which are multifarious interrelationships and dependencies between features. To be considered as practical and realizable, an adversary must ensure that the adversarial examples comply with domain constraints. Recently, generative models like GANs and VAEs have been extensively used for generating adversarial examples against IDS. However, majority of these techniques generate adversarial examples which do not satisfy all domain constraints. Also, current generative methods lack explicit restrictions on the amount of perturbation which a malicious data sample undergoes during the crafting of adversarial examples, leading to the potential generation of invalid data samples. To address these limitations, a solution is presented in this work which utilize a variational autoencoder to generate adversarial examples that not only result in misclassification by an IDS, but also satisfy domain constraints. Instead of perturbing the data samples itself, the adversarial examples are crafted by perturbing the latent space representation of the data sample. It allows the generation of adversarial examples under limited perturbation. This research has explored the novel applications of generative networks for generating constraint satisfying adversarial examples. The experimental results support the claims with an attack success rate of 64.8%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}

关键词： Adversarial example Domain constraints Deep learning Machine learning variational autoencoder

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Dynamical mode recognition of coupled flame oscillators by supervised and unsupervised learning approaches

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KNOWLEDGE-BASED SYSTEMS 2025年 307卷

作者： Xu, Weiming Yang, Tao Zhang, Peng City Univ Hong Kong Dept Mech Engn Kowloon Tong Kowloon Hong Kong 999077 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hung Hong Kowloon Hong Kong 999077 Peoples R China

Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential for ensuring the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core task. To facilitate fundamental studies, the present work concerned dynamical mode recognition of coupled flame oscillators made of flickering buoyant diffusion flames, which have gained increasing attention in recent years but are not sufficiently understood. The time series data of flame oscillators were generated through fully validated reacting flow simulations. Due to the limitations of expertise-based models, a data-driven approach was adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) was used to project the high dimensional data onto a 2-dimensional latent space. Based on phase trajectories in the latent space, both supervised and unsupervised classifiers were proposed for datasets with and without well-known labeling, respectively. For labeled datasets, we established the Wassersteindistance-based classifier (WDC) for mode recognition;for unlabeled datasets, we developed a novel unsupervised classifier (GMM-DTW) combining dynamic time warping (DTW) and Gaussian mixture model (GMM). Through comparing with conventional approaches for dimensionality reduction and classification, the proposed supervised and unsupervised VAE-based approaches exhibit a prominent performance across seven assessment metrics for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition in complex combustion problems.

关键词： variational autoencoder Dynamical mode recognition Coupled flames Wasserstein distance Dynamic time warping

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Uncertainty quantification for variational Bayesian dropout based deep bidirectional LSTM networks

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2025年 1-18页

作者： Sardar, Iqra Noor, Farzana Iqbal, Muhammad Javed Alsanad, Ahmed Akbar, Muhammad Azeem Int Islamic Univ Dept Math & Stat Islamabad 44000 Pakistan Univ Engn & Technol Taxila Dept Comp Sci Taxila 47050 Pakistan King Saud Univ Coll Comp & Informat Sci STCs Artificial Intelligence Chair Dept Informat Syst Riyadh 11451 Saudi Arabia Lappeenranta Lahti Univ Technol Software Engn Dept Lappeenranta 53851 Finland

Time series classification is a critical task in various domains, requiring robust models to handle inherent uncertainties in temporal data. These uncertainties, categorized as aleatoric and epistemic, pose significant challenges in achieving accurate predictions. In real-world applications, models often encounter unseen data that were not present during training process. Bayesian inference has been widely utilized for uncertainty quantification in statistics and machine learning. In this study, we proposed a Bayesian Deep Bi-LSTM model incorporating variational Bayesian dropout with a Gaussian prior and variational autoencoder (VAE). The proposed technique efficiently handles uncertainty in both the model and data while VAE reducing the dimensionality of model parameters. We apply this framework to univariate time series datasets from the UCR repository and compare its performance with four traditional machine learning methods and four sequential deep learning models. Experimental results demonstrate that the Bayesian deep Bi-LSTM model effectively improves overall classification performance. In particular, the model benefits significantly from data augmentation using SMOTE when handling imbalanced dataset. The variational Bayesian dropout model exhibits lower total uncertainty across both datasets, indicating more stable and reliable predictions compared to the VAE-based model. Future research should explore additional datasets from the UCR repository and investigate advanced uncertainty modeling techniques to further enhance performance and scalability.

关键词： Time series classification Bayesian deep Bi-LSTM Aleatoric and epistemic uncertainty variational Bayesian dropout variational autoencoder

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A recombination generation method for missing fault data samples under unknown working conditions based on swap invariance-based multi-attribute disentangled representation network

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MEASUREMENT 2025年 249卷

作者： Cheng, Yujie Wang, Gaowei Gu, Haoxin Zhou, An Lu, Chen Ding, Yu Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Beihang Univ Hangzhou Int Innovat Inst Hangzhou Peoples R China Beihang Univ Sci & Technol Lab Reliabil & Environm Engn Beijing 100191 Peoples R China

Fault diagnosis under variable working conditions has become a prominent research focus in recent years. Numerous methods have been proposed to address it and data-driven methods have become popular. A datadriven diagnostic model with high accuracy is required to be trained on data that fully cover all working conditions. However, collecting such data is difficult, hindering the construction of diagnostic models under variable working conditions. Based on the theory that genetic traits are controlled by genes, this study proposes a recombination generation method for missing fault data samples under unknown working conditions based on a swap invariance-based multi-attribute disentangled representation network (SIMDRN). The proposed method can generate data samples under unknown working conditions based on the existing data under known working conditions. Firstly, three reconstruction losses and a generative adversarial loss are used to train the SIMDRN built using an improved variational autoencoder. Secondly, auxiliary samples are selected and encoded as disentangled factors using the trained SIMDRN. Finally, the missing samples under specific working conditions are generated by recombining and decoding the disentangled factors to improve the diagnostic performance of the models. The proposed method is verified using a superposed sine signal dataset and an experimental rollingbearing fault dataset. The results show that the samples generated by SIMDRN have great diversity and consistency. Moreover, the diagnostic accuracy is significantly improved by combining the generated samples with existing samples.

关键词： Fault diagnosis Variable working conditions variational autoencoder Disentangled representation learning Zero-shot learning

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